Agricultural non-CO2 emissions (Figure 2.25)¶

Notebook sr15_2.4.4_afolu_emissions¶

This notebook is based on the Release 1.1 of the IAMC 1.5C Scenario Explorer and Data and refers to the published version of the IPCC Special Report on Global Warming of 1.5C (SR15).

The notebook is run with pyam release 0.5.0.

The source code of this notebook is available on GitHub (release 2.0.2).

sr15_2.4.4_afolu_emissions

IPCC SR15 scenario assessment¶

Analysis of non-CO2 emissions from agriculture, forestry and land use¶

This notebook plots figures for emissions of CH4 and N2O from the land use sector for Figure 2.25 of the IPCC's "Special Report on Global Warming of 1.5°C".

The scenario data used in this analysis can be accessed and downloaded at https://data.ene.iiasa.ac.at/iamc-1.5c-explorer.

Load `pyam` package and other dependencies¶

In [1]:

import pandas as pd
import numpy as np
import io
import itertools
import yaml
import math
import matplotlib.pyplot as plt
plt.style.use('style_sr15.mplstyle')
%matplotlib inline
import pyam

from utils import boxplot_by_cat

pyam - INFO: Running in a notebook, setting `pyam` logging level to `logging.INFO` and adding stderr handler

Import scenario data, categorization and specifications files¶

The metadata file with scenario categorisation and quantitative indicators can be downloaded at https://data.ene.iiasa.ac.at/iamc-1.5c-explorer. Alternatively, it can be re-created using the notebook sr15_2.0_categories_indicators.

The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook.

In [2]:

sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')

pyam.utils - INFO: Reading `../data/iamc15_scenario_data_world_r2.0.xlsx`

In [3]:

sr1p5.load_meta('sr15_metadata_indicators.xlsx')

pyam.core - INFO: Importing metadata for 416 scenarios (for total of 416)

In [4]:

with open("sr15_specs.yaml", 'r') as stream:
    specs = yaml.load(stream, Loader=yaml.FullLoader)

rc = pyam.run_control()
for item in specs.pop('run_control').items():
    rc.update({item[0]: item[1]})
cats = specs.pop('cats')
all_cats = specs.pop('all_cats')
subcats = specs.pop('subcats')
all_subcats = specs.pop('all_subcats')
marker= specs.pop('marker')

Downselect scenario ensemble to categories of interest for this assessment¶

In [5]:

cats.remove('Above 2C')

In [6]:

years = [2010, 2030, 2050, 2100]

In [7]:

sr1p5.meta.rename(columns={'Kyoto-GHG|2010 (SAR)': 'kyoto_ghg_2010'}, inplace=True)

In [8]:

df = sr1p5.filter(kyoto_ghg_2010='in range', category=cats, year=years)

Set specifications for filter and plotting¶

In [9]:

plot_ylabel = 'Global {} emissions from AFOLU ({})'
save_name = 'output/fig2.25{}.{}'
data_header = 'Emissions from AFOLU'

In [10]:

filter_args = dict(df=sr1p5, category=cats, marker=None, join_meta=True)

In [11]:

def plotting_args(name, label, unit, panel_label=None, filetype='png'):
    return {'categories': cats, 'column': 'category', 'years': years,
            'add_marker': marker,
            'ylabel': plot_ylabel.format(label, unit),
            'save': save_name.format(name if panel_label is None else '{}_{}'.format(panel_label, name), filetype)}

In [12]:

data = []

Methane emissions from agriculture¶

In [13]:

ch4_afolu = df.filter(variable='Emissions|CH4|AFOLU').timeseries()

In [14]:

name = 'ch4_afolu'
species = 'CH4'
unit = 'MtCH4/yr'

_data = pyam.filter_by_meta(ch4_afolu, **filter_args)
boxplot_by_cat(_data, **plotting_args(name, species, unit, 'a'))
_data['species'] = species
data.append(_data)

Nitrous oxide emissions from agriculture¶

In [15]:

n2o_afolu = (
    df.filter(variable='Emissions|N2O|AFOLU')
    .convert_unit('kt N2O/yr', 'Mt N2O/yr', factor=1/1000)
    .timeseries()
)

In [16]:

name = 'no2_afolu'
species = 'N2O'
unit = 'MtN2O'

_data = pyam.filter_by_meta(n2o_afolu, **filter_args)
fig = boxplot_by_cat(_data, **plotting_args(name, species, unit, 'b'),
                     legend=False)
_data['species'] = species
data.append(_data)

Export timeseries data to `xlsx`¶

In [17]:

data = pd.concat(data).set_index(['species', 'category', 'marker'], append=True)

In [18]:

data.head()

Out[18]:

								2010	2030	2050	2100
model	scenario	region	variable	unit	species	category	marker
GCAM 4.2	SSP1-26	World	Emissions\|CH4\|AFOLU	Mt CH4/yr	CH4	Lower 2C	NaN	175.141841	164.524249	156.275277	108.393069
REMIND-MAgPIE 1.7-3.0	PEP_2C_red_goodpractice	World	Emissions\|CH4\|AFOLU	Mt CH4/yr	CH4	Lower 2C	NaN	181.441600	234.163100	174.610000	133.665700
AIM/CGE 2.0	SFCM_SSP2_combined_2Degree	World	Emissions\|CH4\|AFOLU	Mt CH4/yr	CH4	Higher 2C	NaN	180.391200	167.105200	140.526600	150.026000
IMAGE 3.0.1	ADVANCE_2020_WB2C	World	Emissions\|CH4\|AFOLU	Mt CH4/yr	CH4	Lower 2C	NaN	165.800284	155.889552	121.107287	108.867928
IMAGE 3.0.1	SSP4-26	World	Emissions\|CH4\|AFOLU	Mt CH4/yr	CH4	Lower 2C	NaN	165.313137	152.760207	125.057195	110.662131

In [19]:

data.reset_index().to_excel('output/fig2.25_data_table.xlsx')

In [ ]: